FROST -- Fast row-stochastic optimization with uncoordinated step-sizes

In this paper, we discuss distributed optimization over directed graphs, where doubly-stochastic weights cannot be constructed. Most of the existing algorithms overcome this issue by applying push-sum consensus, which utilizes column-stochastic weights. The formulation of column-stochastic weights requires each agent to know (at least) its out-degree, which may be impractical in e.g., broadcast-based communication protocols. In contrast, we describe FROST (Fast Row-stochastic-Optimization with uncoordinated STep-sizes), an optimization algorithm applicable to directed graphs that does not require the knowledge of out-degrees; the implementation of which is straightforward as each agent locally assigns weights to the incoming information and locally chooses a suitable step-size. We show that FROST converges linearly to the optimal solution for smooth and strongly-convex functions given that the largest step-size is positive and sufficiently small.Comment: Submitted for journal publication, currently under revie

DICER1 regulated let-7 expression levels in p53-induced cancer repression requires cyclin D1.

Let-7 miRNAs act as tumour suppressors by directly binding to the 3\u27UTRs of downstream gene products. The regulatory role of let-7 in downstream gene expression has gained much interest in the cancer research community, as it controls multiple biological functions and determines cell fates. For example, one target of the let-7 family is cyclin D1, which promotes G0/S cell cycle progression and oncogenesis, was correlated with endoribonuclease DICER1, another target of let-7. Down-regulated let-7 has been identified in many types of tumours, suggesting a feedback loop may exist between let-7 and cyclin D1. A potential player in the proposed feedback relationship is Dicer, a central regulator of miRNA expression through sequence-specific silencing. We first identified that DICER1 is the key downstream gene for cyclin D1-induced let-7 expression. In addition, we found that let-7 miRNAs expression decreased because of the p53-induced cell death response, with deregulated cyclin D1. Our results also showed that cyclin D1 is required for Nutlin-3 and TAX-induced let-7 expression in cancer repression and the cell death response. For the first time, we provide evidence that let-7 and cyclin D1 form a feedback loop in regulating therapy response of cancer cells and cancer stem cells, and importantly, that alteration of let-7 expression, mainly caused by cyclin D1, is a sensitive indicator for better chemotherapies response

The endogenous cell-fate factor dachshund restrains prostate epithelial cell migration via repression of cytokine secretion via a cxcl signaling module.

Prostate cancer is the second leading form of cancer-related death in men. In a subset of prostate cancer patients, increased chemokine signaling IL8 and IL6 correlates with castrate-resistant prostate cancer (CRPC). IL8 and IL6 are produced by prostate epithelial cells and promote prostate cancer cell invasion; however, the mechanisms restraining prostate epithelial cell cytokine secretion are poorly understood. Herein, the cell-fate determinant factor DACH1 inhibited CRPC tumor growth in mice. Using Dach1(fl/fl)/Probasin-Cre bitransgenic mice, we show IL8 and IL6 secretion was altered by approximately 1,000-fold by endogenous Dach1. Endogenous Dach1 is shown to serve as a key endogenous restraint to prostate epithelial cell growth and restrains migration via CXCL signaling. DACH1 inhibited expression, transcription, and secretion of the CXCL genes (IL8 and IL6) by binding to their promoter regulatory regions in chromatin. DACH1 is thus a newly defined determinant of benign and malignant prostate epithelium cellular growth, migration, and cytokine abundance in vivo

Msmsfnet: a multi-stream and multi-scale fusion net for edge detection

Edge detection is a long standing problem in computer vision. Recent deep learning based algorithms achieve state of-the-art performance in publicly available datasets. Despite the efficiency of these algorithms, their performance, however, relies heavily on the pretrained weights of the backbone network on the ImageNet dataset. This limits heavily the design space of deep learning based edge detectors. Whenever we want to devise a new model, we have to train this new model on the ImageNet dataset first, and then fine tune the model using the edge detection datasets. The comparison would be unfair otherwise. However, it is usually not feasible for many researchers to train a model on the ImageNet dataset due to the limited computation resources. In this work, we study the performance that can be achieved by state-of-the-art deep learning based edge detectors in publicly available datasets when they are trained from scratch, and devise a new network architecture, the multi-stream and multi scale fusion net (msmsfnet), for edge detection. We show in our experiments that by training all models from scratch to ensure the fairness of comparison, out model outperforms state-of-the art deep learning based edge detectors in three publicly available datasets

Design and Control of a Novel Bionic Mantis Shrimp Robot

This article presents the development of a novel bionic robot, which is inspired by agile and fast mantis shrimp in the ocean. The developed bionic mantis shrimp robot has ten rigid-flexible swimming feet (pleopods) for swimming propulsion and a rope-driven spine for its body bending. By studying the motion trajectory of biological mantis shrimp, the kinematic gait planning of the bionic pleopod is completed and the central pattern generator controller of the bionic mantis shrimp robot applicable to the coupled motion of multiple pleopods is proposed. The controller is experimentally verified to effectively simulate the swimming motion of mantis shrimp, which enables the robot to reach a maximum swimming velocity of 0.28 m/s (0.46 body length per second) and a minimum turning radius of 0.36 m.The influence of control parameters on the robot's swimming performance is then investigated. Experiments are conducted to show that the oscillation frequency of the bionic pleopod plays a major positive role in the robot's swimming speed. This article has demonstrated the effectiveness of the proposed mechanism design and motion control method for a bionic mantis shrimp robot and laid the foundation for the further exploration of bionic mantis shrimp robots in rugged seabed environments